Search the podcast catalog by what is said in episodes — by meaning (semantic_search), by exact phrase (keyword_search), or both at once (hybrid ranking). This is THE way to retrieve relevant dialogue, segments, and clips: each result is one segment of one episode with bounded transcript windows pinpointing the highest-relevance lines, plus any highlight clips that overlap the segment inline on the match.

Segments partition an episode's transcript — where start_line and end_line are present, every spoken line belongs to exactly one segment and one segment's end_line + 1 is the next one's start_line. They are contiguous in transcript lines, not in wall-clock seconds: the seconds between one segment's end_seconds and the next's start_seconds contain no transcribed speech. These matches do not carry the line ranges themselves — fetch them with particle_podcast_get_episode and include: ["segments"], where their absence marks an episode segmented by an earlier version, a small share of which do leave lines uncovered. Clips are sparse, engagement-ranked highlights that overlap some segments. There is no separate clip-search tool — relevant clips arrive on these matches, and a known episode's full clip list is particle_podcast_get_episode with include: ["clips"].

A match window defaults to one line of context around each matched line; raise context to widen windows in place instead of fetching the full transcript.

Screening many results? Pass format: "compact". Each match then carries only its identity — episode and podcast slugs, segment id and bounds, segment type, the segment's one-line description, and the relevance score — with no dialogue or clips, at a fraction of the size and latency of the default. Fan out compact searches over companies, themes, or dates, decide which segments matter, then read dialogue only for those: particle_podcast_get_episode with include: ["transcript"] and transcript_start/transcript_end set to the segment's bounds, or this tool again with episode_slug narrowed to that episode.

Use this for "find dialogue about a topic". For "every line naming a person or company" use particle_podcast_find_mentions instead — person_slug and company_slug here narrow ranked results, they don't drive the ranking.

Choosing your query. At least one of semantic_search or keyword_search is required, and they do different jobs:

• semantic_search carries the idea. Write it as a sentence describing what should be discussed, in the vocabulary a speaker would use. It is paraphrase-tolerant, so it finds the topic however it happens to be worded.

• keyword_search carries words that must be literally spoken. Every word must occur in the same passage, so it is for one or two exact tokens — a ticker, a product name — not for a description. Putting a sentence here returns nothing.

• Use both when a topic must also contain an exact term. The result is their intersection, which is narrow by design; if that comes back empty, keyword_match: "ranked" relaxes the keyword side to a relevance hint.

Do not put a name in semantic_search. Resolve it (particle_person_resolve, particle_company_resolve, particle_entity_resolve) and pass the slug — searching for "Sam Altman" as text finds passages that sound like him, while person_slug finds the episodes actually featuring him.

Start broad, then narrow. Every filter compounds, and each one can silently remove all results. Issue the query with semantic_search alone first, then add filters once you know the topic has coverage. If a search returns nothing because of your filters, the error names the specific parameter responsible and the retry to make — act on it rather than re-issuing variations of the same query.

Note on role. It describes how someone relates to the episode: guest/host/panelist/correspondent mean they spoke, mention means they were talked about. Omitting role covers both and is almost always what you want.

Guests' expertise. The guest_ filters keep episodes with a guest of that expertise (codes from particle_expertise_resolve), e.g. what practicing cardiologists said about a drug.